Image interpolation is widely used in applications such as video de-interlacing, video scaling, etc. in digital TV systems. An effective image interpolation technique is important for the overall image quality in a digital TV system. Conventionally, interpolation processes are performed such that a new sample is generated based on the values of original image samples that are neighboring to the new sample position as well as the relative locations of the original image samples to the new sample. Take one dimensional interpolation as an example, if q represents the interpolated sample, and p0 and p1 represent the two closest neighboring pixels of q, then the interpolation value q may be conventionally calculated as q=d0*p1+d1*p0, wherein the distances to q from pixel p0 and p1 are expressed as d0 and d1, respectively. Here, the distance between two original neighboring pixels is assumed to be one, i.e. d0+d1=1.
However, the conventional interpolation method described above has the undesirable effect of smoothing edges present in the original image. For example, assuming that originally there is a sharp luminance transition between the locations of pixels p0 and p1 in the original image, then using the above interpolation method the transition becomes smoother in the interpolated image as new samples are interpolated between the locations of p0 and p1.
Using FIR (Finite Impulse Response) digital filters, more neighboring pixels can be utilized in interpolating a new sample. Though experiments show that edge sharpness can be improved when more neighboring pixels are utilized in interpolation, generally edges in the interpolated output image still tend to be smoother than those in the original input image.
There is, therefore, a need for a method and apparatus for video image interpolation with edge sharpening and enhancement during the image interpolation process such that edge sharpness can be substantially preserved and/or enhanced in the interpolated image.